Complete List » Collaborative filtering

Publications Related to Collaborative filtering

  1. Multi-task Learning for Recommender Systems.

    Xia Ning and George Karypis. 2nd Asian Conference on Machine Learning (ACML), 2010.

  1. A Novel Approach to Compute Similarities and its Application to Item Recommendation.

    Christian Desrosiers and George Karypis. 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI), pp. 39—51, 2010.

  1. Learning Preferences of New Users in Recommender Systems: An Information Theoretic Approach.

    Al Mamunnur Rashid, George Karypis, and John Riedl. SIGKDD Workshop on Web Mining and Web Usage Analysis (WEBKDD), 2008.

  1. Towards a Scalable kNN CF Algorithm: Exploring Effective Applications of clustering.

    Al Mamunur Rashid, Shyong K. Lam, Adam LaPitz, George Karypis, and John Riedl. In “Web Mining and Web Usage Analysis”, O. Nasraoui, et. al. (editors), Springer, 2008.

  1. ClustKNN: A Highly Scalable Hybrid Model- and Memory-Based CF Algorithm.

    Al Mamunur Rashid, Shyong K. Lam, George Karypis, and John Riedl. WEBKDD, 2006.

  1. Influence in Ratings-Based Recommender Systems: An Algorithm-Independent Approach.

    Al Mamunur Rashid, George Karypis, and John Riedl. SIAM International conference on Data Mining, 2005.

  1. Feature-Based Recommendation System.

    Eui-Hong (Sam) Han and George Karypis. Proceedings of the 14th Conference of Information and Knowledge Management (CIKM), pp. 446 - 452, 2005.

  1. Item-Based Top-N Recommendation Algorithms.

    Mukund Deshpande and George Karypis. ACM Transactions on Information Systems. Volume 22, Issue 1, pp. 143 - 177, 2004.

  1. Recommender Systems for Large-Scale E-Commerce: Scalable Neighborhood Formation Using Clustering.

    Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 5th International Conference on Computer and Information Technology (ICCIT), 2002.

  1. Incremental SVD-Based Algorithms for Highly Scalable Recommender Systems.

    Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 5th International Conference on Computer and Information Technology (ICCIT), 2002.

  1. When being Weak is Brave: Privacy Issues in Recommender Systems.

    N. Ramakrishnan, B. J. Keller, B. J. Mirza, A. Y. Grama, and G. Karypis. IEEE Internet Computing, 54 - 62, Vol 5, No. 6, 2001.

  1. Item-Based Collaborative Filtering Recommendation Algorithms.

    Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. WWW10, pp. 285 - 295, 2001.